Therefore, an important capability is to separate the deformation due to the manner of contact from that due to the stimulus geometry. For example, when continuously exploring an object surface, the sliding-induced shear would need to be removed from tactile image to extract the local contact shape. Previous work on extracting pose information from contact to continuously slide a tactile sensor over complex objects used a deep convolutional neural network trained to map between shear-distorted inputs and the desired (pose) outputs, which was necessary for sliding exploration over a range of complex novel objects [1]}, [2]}, [3]}. Here, we suggest, an alternate disentanglement based approach that learns to remove motion-induced shear from raw sheared sensor response that has several advantages over previous task-specific approach of training insensitivity to shear, in that: (a) it is computationally and data efficient by only requiring the ‘unshearing’ of the tactile data to be learnt once instead of relearning for each tactile task, such as when applied to full object reconstruction; (b) the models can then be learnt more easily on unsheared data, which we demonstrate by predicting local object pose; moreover, in some cases no model need be learnt at all, which we demonstrate with contact surface reconstruction using a Voronoi tessellation from the tactile marker data; and (c) the methods are extensible to including other downstream variables of interest appropriate to the task, rather than having to completely retrain new models from scratch.
